Keywords
Advanced Wound Care
Technologies
How to Cite
Abstract
Introduction. Wounds represent a substantial and growing global health burden, driven by aging populations, chronic disease, and rising healthcare costs. Artificial intelligence (AI) has emerged as a potential tool to improve wound assessment, monitoring, and management.
Aim. To comment on the current state of AI in wound care and identify key requirements for successful clinical translation.
Key Content. Recent advances demonstrate that AI can support wound classification, measurement, tissue characterization, healing prediction, and remote monitoring. Increasingly, AI is being positioned within theragnostic frameworks that integrate diagnosis, monitoring, and personalized intervention. Despite this progress, most applications remain investigational, with limited real-world validation, fragmented datasets, and unresolved ethical and regulatory challenges.
Implications. AI should be viewed as a clinical decision-support technology rather than an autonomous replacement for expert care.
Conclusion. Realizing the clinical value of AI in wound care will require robust evidence, standardized data infrastructures, ethical governance, and meaningful clinician engagement.
References
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